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Colloque - Valentin Wyart : The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition
Stanislas Dehaene
Chaire Psychologie cognitive expérimentale
Année 2025-2026
Collège de France
Colloque : Seeing the Mind, Educating the Brain
Theme: Human Singularity
The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition
Colloque - Valentin Wyart : The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition
Valentin Wyart
Résumé
Quantitative modeling approaches are routinely used in cognitive science to make sense of behavior. Statistical models are designed to test *what* specific patterns are present in behavior, whereas cognitive computational models are developed to describe *how* specific behavioral patterns may emerge from latent cognitive processes. These two types of modeling approaches have successfully identified characteristic (and sometimes suboptimal) features of human learning and decision-making under uncertainty. In this talk, I will argue that cognitive computational models can be used to answer the distinct question of *why* these characteristic features are there. I will use recent studies that rely on different classes of models (low-dimensional algorithmic models, high-dimensional neural networks) to explain characteristic features of human cognition in terms of latent objectives and constraints.
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By Collège de France
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11 ratings
Stanislas Dehaene
Chaire Psychologie cognitive expérimentale
Année 2025-2026
Collège de France
Colloque : Seeing the Mind, Educating the Brain
Theme: Human Singularity
The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition
Colloque - Valentin Wyart : The What?, How? And Why? Of Behavior: Using Cognitive Computational Models to Answer Distinct Questions about Human Cognition
Valentin Wyart
Résumé
Quantitative modeling approaches are routinely used in cognitive science to make sense of behavior. Statistical models are designed to test *what* specific patterns are present in behavior, whereas cognitive computational models are developed to describe *how* specific behavioral patterns may emerge from latent cognitive processes. These two types of modeling approaches have successfully identified characteristic (and sometimes suboptimal) features of human learning and decision-making under uncertainty. In this talk, I will argue that cognitive computational models can be used to answer the distinct question of *why* these characteristic features are there. I will use recent studies that rely on different classes of models (low-dimensional algorithmic models, high-dimensional neural networks) to explain characteristic features of human cognition in terms of latent objectives and constraints.
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